All kinds of contributions are welcome, including but not limited to the following.
- Fix typo or bugs
- Add documentation or translate the documentation into other languages
- Add new features and components
## Workflow
1. fork and pull the latest OpenMMLab repository (MMClassification)
2. checkout a new branch (do not use master branch for PRs)
3. commit your changes
4. create a PR
```{note}
If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
```
## Code style
### Python
We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
We use the following tools for linting and formatting:
-[flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.
-[isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
-[yapf](https://github.com/google/yapf): A formatter for Python files.
-[codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.
-[mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.
-[docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
Style configurations can be found in [setup.cfg](./setup.cfg).
We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
The config for a pre-commit hook is stored in [.pre-commit-config](https://github.com/open-mmlab/mmclassification/blob/master/.pre-commit-config.yaml).
After you clone the repository, you will need to install initialize pre-commit hook.
```shell
pip install-U pre-commit
```
From the repository folder
```shell
pre-commit install
```
After this on every commit check code linters and formatter will be enforced.
```{important}
Before you create a PR, make sure that your code lints and is formatted by yapf.
```
### C++ and CUDA
We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
:point_right: **MMClassification 1.0 branch is in trial, welcome every to [try it](https://github.com/open-mmlab/mmclassification/tree/1.x) and [discuss with us](https://github.com/open-mmlab/mmclassification/discussions)!** :point_left:
</div>
## Introduction
English | [简体中文](/README_zh-CN.md) | [模型的测试方法及测试步骤](train.md)
MMClassification is an open source image classification toolbox based on PyTorch. It is
a part of the [OpenMMLab](https://openmmlab.com/) project.
Please refer to [install.md](https://mmclassification.readthedocs.io/en/latest/install.html) for more detailed installation and dataset preparation.
## Getting Started
Please see [Getting Started](https://mmclassification.readthedocs.io/en/latest/getting_started.html) for the basic usage of MMClassification. There are also tutorials:
-[Learn about Configs](https://mmclassification.readthedocs.io/en/latest/tutorials/config.html)
- Learn about MMClassification **Python API**: [Preview the notebook](https://github.com/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_python.ipynb) or directly [run on Colab](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_python.ipynb).
- Learn about MMClassification **CLI tools**: [Preview the notebook](https://github.com/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_tools.ipynb) or directly [run on Colab](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_tools.ipynb).
## Model zoo
Results and models are available in the [model zoo](https://mmclassification.readthedocs.io/en/latest/model_zoo.html).
We appreciate all contributions to improve MMClassification.
Please refer to [CONTRUBUTING.md](https://mmclassification.readthedocs.io/en/latest/community/CONTRIBUTING.html) for the contributing guideline.
## Acknowledgement
MMClassification is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new classifiers.
## Citation
If you find this project useful in your research, please consider cite:
```BibTeX
@misc{2020mmclassification,
title={OpenMMLab's Image Classification Toolbox and Benchmark},
